sf-quant 0.2.6

Python package for Silver Fund quant team research and trading tools.

Silver Fund Quant

A quantitative finance library for the BYU Silver Fund quant team. Provides tools for data loading, portfolio optimization, backtesting, performance analysis, and signal research — all built on Polars DataFrames.

Installation

pip install sf-quant

Environment Configuration

sf-quant requires two environment variables to locate data: ROOT and DATABASE.

Option 1: .env file or environment variables

Create a .env file in your project root (automatically loaded via python-dotenv):

ROOT=/path/to/root
DATABASE=your_database

Or export them in your shell:

export ROOT=/path/to/root
export DATABASE=your_database

Option 2: Programmatic configuration

Set values at runtime using sfd.env():

import sf_quant.data as sfd

sfd.env(root="/path/to/root", database="your_database")

This will override any values set via environment variables.

Quick Start

A complete workflow: load asset data, build a momentum alpha signal, construct a covariance matrix, and optimize a portfolio.

import sf_quant.data as sfd
import sf_quant.optimizer as sfo
import datetime as dt
import polars as pl

# 1. Load asset data
df = (
    sfd.load_assets(
        start=dt.date(2024, 1, 1),
        end=dt.date(2024, 12, 31),
        columns=["date", "barrid", "price", "return", "specific_risk", "predicted_beta"],
        in_universe=True,
    )
    .with_columns(pl.col("return", "specific_risk").truediv(100))
    .sort("barrid", "date")
)

# 2. Build a momentum alpha signal
df_clean = (
    df
    .with_columns(
        pl.col("return").log1p().rolling_sum(window_size=230).shift(22).over("barrid").alias("momentum"),
    )
    .with_columns(
        pl.col("momentum").sub(pl.col("momentum").mean()).truediv(pl.col("momentum").std()).over("date").alias("score")
    )
    .with_columns(
        pl.lit(0.05).mul(pl.col("score")).mul(pl.col("specific_risk")).alias("alpha")
    )
    .drop_nulls()
    .sort("barrid")
)

# 3. Construct the covariance matrix
date_ = df_clean["date"].last()
barrids = df_clean["barrid"].to_list()

covariance_matrix = (
    sfd.construct_covariance_matrix(date_=date_, barrids=barrids)
    .drop("barrid").to_numpy()
)

# 4. Optimize the portfolio
weights = sfo.mve_optimizer(
    ids=barrids,
    alphas=df_clean["alpha"].to_list(),
    betas=df_clean["predicted_beta"].to_list(),
    covariance_matrix=covariance_matrix,
    constraints=[sfo.ZeroBeta()],
    gamma=100,
)

Modules

sf_quant.data

Load financial data as Polars DataFrames. Use get_*_columns() to inspect available columns for any dataset.

Function	Description
load_assets()	Asset prices, returns, risk, and metadata for a date range
load_assets_by_date()	Asset data for a single date
load_crsp_daily() / load_crsp_v2_daily()	CRSP daily stock data
load_crsp_monthly() / load_crsp_v2_monthly()	CRSP monthly stock data
load_exposures() / load_exposures_by_date()	Barra factor exposures (77+ factors)
load_covariances_by_date()	Factor covariance matrix for a date
construct_covariance_matrix()	Build an asset-level covariance matrix from the factor model
load_benchmark() / load_benchmark_returns()	Benchmark weights and returns
load_factors() / get_factor_names()	Barra factor returns and metadata
load_fama_french()	Fama-French 5-factor data

sf_quant.optimizer

Mean-variance efficient (MVE) portfolio optimizer built on CVXPY.

import sf_quant.optimizer as sfo

weights = sfo.mve_optimizer(
    ids=barrids,
    alphas=alphas,
    covariance_matrix=covariance_matrix,
    constraints=[sfo.FullInvestment(), sfo.LongOnly()],
    gamma=2,
)

Available constraints:

Constraint	Effect
FullInvestment()	Weights sum to 1
ZeroInvestment()	Weights sum to 0
LongOnly()	No short positions
NoBuyingOnMargin()	Weights sum to at most 1
UnitBeta()	Portfolio beta equals 1
ZeroBeta()	Portfolio beta equals 0

sf_quant.backtester

Run walk-forward backtests that optimize at each rebalance date.

import sf_quant.backtester as sfb

# Sequential (single core)
portfolio = sfb.backtest_sequential(data=df, constraints=constraints, gamma=2)

# Parallel (multi-core via Ray)
portfolio = sfb.backtest_parallel(data=df, constraints=constraints, gamma=2, n_cpus=4)

sf_quant.performance

Compute and visualize portfolio performance metrics.

Function	Description
generate_returns_from_weights()	Portfolio returns from weight history
generate_multi_returns_from_weights()	Total, benchmark, and active returns
generate_leverage_from_weights()	Sum of absolute weights over time
generate_drawdown_from_returns()	Drawdown from peak
generate_returns_summary_table()	Mean return, volatility, Sharpe, total return
generate_alpha_ics()	Information coefficients between signals and realized returns
get_turnover_stats()	Rolling turnover statistics
generate_returns_chart()	Plot cumulative returns
generate_multi_returns_chart()	Plot total/benchmark/active returns
generate_leverage_chart()	Plot leverage over time
generate_drawdown_chart()	Plot drawdowns
generate_ic_chart()	Plot information coefficients
plot_turnover()	Plot portfolio turnover

sf_quant.research

Tools for signal analysis and factor research.

Function	Description
generate_quantile_ports()	Sort assets into quantile portfolios with long-short spread
vol_scale_ports()	Scale portfolio returns to target volatility
beta_scale_ports()	Scale portfolio returns to target beta
run_ff_regression()	Fama-French 5-factor regression
run_quantile_ff_regression()	FF regressions across quantile portfolios
get_signal_stats()	Summary statistics for a signal
get_signal_distribution()	Plot signal distribution

Documentation Development

To run a local server of the sphinx documentation run

uv run sphinx-autobuild docs docs/_build/html

Release Process

1. Create PR
2. Merge PR(s)
3. Increment version in pyproject.toml
4. git tag v*..
5. git push origin main --tags
6. Create a release and publish release notes (github)
7. uv build
8. uv publish